Speaker
Sitian Qian
(NU FNAL)
Description
The release of LEP open data provides a clean test bed for developing machine-learning methods relevant to future lepton colliders. We present an effort to identify individual b hadrons in DELPHI $Z\to b\bar{b}$ events using an approach inspired by panoptic segmentation. Rather than assigning a single flavour label to a jet, the model classifies reconstructed particles and associates them with the decay products of each b hadron. We propose a design of conversion to EDM4HEP format for DELPHI open-data, the formulation of particle-to-hadron assignment as a segmentation problem, and initial tagging studies. This work explores archival DELPHI data as a benchmark for instance-level heavy-flavour reconstruction.
Author
Sitian Qian
(NU FNAL)